Implement Candidate Elimination Algorithm in JAVA

Program:

package k;

import java.io.*;

import java.util.ArrayList;

import java.util.HashMap;

import java.util.Iterator;

import java.util.List;

import java.util.Map;

public class kl {

                static int sg = 0;

Data Compression using 2D Wavelet Analysis

(Type this code in editor then run it)

load woman;              % Load original image.

image(X)

title('Original Image')

colormap(map)

x = X(100:200,100:200);  % Select ROI

n = 5;                   % Decomposition Level

w = 'sym8';              % Near symmetric wavelet

Python program for visualisation of shapefile

import shapefile
import matplotlib.pyplot as plt
from numpy import array
from numpy import arange
import matplotlib.cm as cm
import matplotlib.patches as patches
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
sf = shapefile.Reader('D:/vector/India/IND_adm3.shp')

recs = sf.records()
shapes = sf.shapes()
Nshp = len(shapes)
cns =[]
for nshp in xrange(Nshp):
    cns.append(recs[nshp][1])
cns = array(cns)
cm = cm.get_cmap('Dark2')
cccol = cm(1.*arange(Nshp)/Nshp)
# --plot--
fig = plt.figure()
ax = fig.add_subplot(111)
for nshp in xrange(Nshp):
    ptchs = []
    pts = array(shapes[nshp].points)
    prt = shapes[nshp].parts
    par = list(prt) + [pts.shape[0]]
    for pij in xrange(len(prt)):
        ptchs.append(Polygon(pts[par[pij]:par[pij + 1]]))
        ax.add_collection(PatchCollection(ptchs,facecolor = cccol[nshp,:],\
                          edgecolor = 'k', linewidths =.1))
ax.set_xlim(-180,+180)
ax.set_ylim(-90,90)
##fig.savefig('test.png')
plt.show()
     

Python program to print countryName, countryCode, minLat, maxLat, minLong,maxLong

from osgeo import ogr

shapefile = ogr.Open('D:/vector/TM_WORLD_BORDERS-0.3/TM_WORLD_BORDERS-0.3.shp')

layer = shapefile.GetLayer(0)
countries =[]
for i in range(layer.GetFeatureCount()):
    feature = layer.GetFeature(i)
    countryCode = feature.GetField('ISO3')
    countryName = feature.GetField('NAME')
    print '{}:{}'.format(countryCode, countryName)
    geometry = feature.GetGeometryRef()
    minLong, maxLong, minLat, maxLat = geometry.GetEnvelope()
    countries.append((countryName, countryCode, minLat, maxLat, minLong,maxLong))


countries.sort()
for name,code, minLat, maxLat, minLong, maxLong in countries:
    print '{} ({}) lat = {:.4f}..{:.4f}..Long={:.4f}..{:.4f}'.format(name, code, minLat, maxLat, minLong, maxLong)

Python program to extract Spatial reference of shape file

from osgeo import ogr

shapefile = ogr.Open('D:/vector/India/IND_adm2.shp')

layer = shapefile.GetLayer(0)
##Get Projected Spatial reference

sr = layer.GetSpatialRef()
print sr

geogr_sr = sr.CloneGeogCS()

Python program to extract attributes of features of shape file

from osgeo import ogr

shapefile = ogr.Open('D:/vector/TM_WORLD_BORDERS-0.3/TM_WORLD_BORDERS-0.3.shp')


if shapefile is not None:
    print 'Data loaded successfully..'
    layers = shapefile.GetLayerCount()
    print 'Shapefile has {} layers'.format(layers)
    layer = shapefile.GetLayer(0)
    features = layer.GetFeatureCount()
    print 'Layer {} has {} features'.format(layers,features)
    feature = layer.GetFeature(82)
    attributes = feature.items()
    print 'Feature 82 has following attributes'
    for key, value in attributes.items():
        print '{} = {} '.format(key,value)

    geometry = feature.GetGeometryRef()
    geometryName = geometry.GetGeometryName()

    print 'Feature\'s geometry data consist of a {}'.format(geometryName)
else:
    print 'Failed to load date'
   

Python program to extract features of shape file

from osgeo import ogr

shapefile = ogr.Open('D:/vector/TM_WORLD_BORDERS-0.3/TM_WORLD_BORDERS-0.3.shp')

if shapefile is not None:
    print 'Data loaded successfully..'
    layers = shapefile.GetLayerCount()
    print 'Shapefile has {} layers'.format(layers)
    for numLayers in range(layers):
        layer = shapefile.GetLayer(numLayers)
        features = layer.GetFeatureCount()
        print 'Layer {} has {} features'.format(numLayers,features)
        for featureNum in range(features):
            feature = layer.GetFeature(featureNum)
            featureName = feature.GetField('NAME')
            print 'Feature {} had name :{}'.format(featureNum, featureName)
else:
    print 'Failed to load date'
   

Program in JAVA to implement Mutual Exclusion.

public class Counter_locks implements Runnable {

      int count = 0;

     public Counter_locks() { 

     }

     public synchronized void run()  //synchronizes the run method

     {

         System.out.println(Thread.currentThread().getName() + ": " + "Current Value of count = " + count);

         System.out.println(Thread.currentThread().getName() + " increments count....");

         count ++;

        try {

                             Thread.currentThread().sleep(1000);

         } catch (InterruptedException e) {

             System.out.println("Thread " + Thread.currentThread().getName() + "interrupted.");

         }

         System.out.println("Value of count after incremented by thread "

         + Thread.currentThread().getName() + " = " + count);

     }

     public static void main(String args[]) {

         Counter_locks c = new Counter_locks();

  

         Thread t1 = new Thread(c);

         Thread t2 = new Thread(c);

         Thread t3 = new Thread(c);

         Thread t4 = new Thread(c);

  

         t1.setName("Thread 1");

         t2.setName("Thread 2");

         t3.setName("Thread 3");

         t4.setName("Thread 4");

  

         t1.start();

         t2.start();

         t3.start();

         t4.start();

  

         try {

             t1.join();

             t2.join();

             t3.join();

             t4.join();

         }

         catch (InterruptedException e) {

             System.out.println("Main thread Interrupted .....");

         }

  

         System.out.println("Main thread exits.....");

     }

 }

Output :

D:\>javac  Counter_locks.java

D:\>java Counter_locks

Thread 1: Current Value of count = 0

Thread 1 increments count....

Value of count after incremented by thread Thread 1 = 1

Thread 4: Current Value of count = 1

Thread 4 increments count....

Value of count after incremented by thread Thread 4 = 2

Thread 3: Current Value of count = 2

Thread 3 increments count....

Value of count after incremented by thread Thread 3 = 3

Thread 2: Current Value of count = 3

Thread 2 increments count....

Value of count after incremented by thread Thread 2 = 4

Main thread exits.....

DeadlockAvoidance (java)

import java.util.Scanner;
public class DeadlockAvoidance{
 private int need[][],allocate[][],max[][],avail[][],np,nr;
  private void input() {  
   Scanner sc=new Scanner(System.in);
     System.out.print("Enter no. of processes and resources : ");
     np=sc.nextInt();  //no. of process
     nr=sc.nextInt();  //no. of resources
     need=new int[np][nr];  //initializing arrays
     max=new int[np][nr];
     allocate=new int[np][nr];
     avail=new int[1][nr];
         System.out.println("Enter allocation matrix -->");
     for(int i=0;i<np;i++)
          for(int j=0;j<nr;j++)
         allocate[i][j]=sc.nextInt();  //allocation matrix
            System.out.println("Enter max matrix -->");
     for(int i=0;i<np;i++)
          for(int j=0;j<nr;j++)
         max[i][j]=sc.nextInt();  //max matrix
               System.out.println("Enter available matrix -->");
        for(int j=0;j<nr;j++)
         avail[0][j]=sc.nextInt();  //available matrix
            sc.close(); }
    private int[][] calc_need(){
       for(int i=0;i<np;i++)
         for(int j=0;j<nr;j++)  //calculating need matrix
          need[i][j]=max[i][j]-allocate[i][j];
           return need;
    }
      private boolean check(int i){
       //checking if all resources for ith process can be allocated
       for(int j=0;j<nr;j++)
       if(avail[0][j]<need[i][j])
          return false;
        return true;
    }
    public void isSafe(){
       input();
       calc_need();
       boolean done[]=new boolean[np];
       int j=0;
       while(j<np){  //until all process allocated
       boolean allocated=false;
       for(int i=0;i<np;i++)
        if(!done[i] && check(i)){  //trying to allocate
            for(int k=0;k<nr;k++)
            avail[0][k]=avail[0][k]-need[i][k]+max[i][k];
         System.out.println("Allocated process : "+i);
         allocated=done[i]=true;
               j++;  }
          if(!allocated) break;  //if no allocation
       }
       if(j==np)  //if all processes are allocated
        System.out.println("\nSafely allocated");
       else
        System.out.println("All proceess cant be allocated safely");    }
      public static void main(String[] args) {
       new DeadlockAvoidance().isSafe();
    }}

DWT CODE IN MATLAB (NO GUI)

i= imread('rice.png');
imshow(i);

MATLAB GUI code for discrete wavelet transform DWT of image

click here to download .m file
click here to download .fig file
CODE :

function varargout = dwtgui(varargin)

% DWTGUI M-file for dwtgui.fig

%      DWTGUI, by itself, creates a new DWTGUI or raises the existing

%      singleton*.

Machine Learning :To Implement GUI program on Delta , Epsilon and N

Aim : To Implement GUI program on Delta , Epsilon and N

DataSet 1:- Diabetes Data Set 

Data Set Information:

Diabetes patient records were obtained from two sources: an automatic electronic recording device and paper records. The automatic device had an internal clock to timestamp events, whereas the paper records only provided "logical time" slots (breakfast, lunch, dinner, bedtime). For paper records, fixed times were assigned to breakfast (08:00), lunch (12:00), dinner (18:00), and bedtime (22:00). Thus paper records have fictitious uniform recording times whereas electronic records have more realistic time stamps. 

Diabetes files consist of four fields per record. Each field is separated by a tab and each record is separated by a newline.

File Names and format:
(1) Date in MM-DD-YYYY format
(2) Time in XX:YY format
(3) Code
(4) Value

The Code field is deciphered as follows:

33 = Regular insulin dose
34 = NPH insulin dose
35 = UltraLente insulin dose
48 = Unspecified blood glucose measurement
57 = Unspecified blood glucose measurement
58 = Pre-breakfast blood glucose measurement
59 = Post-breakfast blood glucose measurement
60 = Pre-lunch blood glucose measurement
61 = Post-lunch blood glucose measurement
62 = Pre-supper blood glucose measurement
63 = Post-supper blood glucose measurement
64 = Pre-snack blood glucose measurement
65 = Hypoglycemic symptoms
66 = Typical meal ingestion
67 = More-than-usual meal ingestion
68 = Less-than-usual meal ingestion
69 = Typical exercise activity
70 = More-than-usual exercise activity
71 = Less-than-usual exercise activity
72 = Unspecified special event

Attribute Information:

Diabetes files consist of four fields per record. Each field is separated by a tab and each record is separated by a newline.

File Names and format:
(1) Date in MM-DD-YYYY format
(2) Time in XX:YY format
(3) Code
(4) Value

DataSet 2:- Car Data Set 

Data Set Information:

Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX, M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.). The model evaluates cars according to the following concept structure:

CAR car acceptability
. PRICE overall price
. . buying buying price
. . maint price of the maintenance
. TECH technical characteristics
. . COMFORT comfort
. . . doors number of doors
. . . persons capacity in terms of persons to carry
. . . lug_boot the size of luggage boot
. . safety estimated safety of the car

Input attributes are printed in lowercase. Besides the target concept (CAR), the model includes three intermediate concepts: PRICE, TECH, COMFORT. Every concept is in the original model related to its lower level descendants by a set of examples (for these examples sets see [Web Link]).

The Car Evaluation Database contains examples with the structural information removed, i.e., directly relates CAR to the six input attributes: buying, maint, doors, persons, lug_boot, safety.

Because of known underlying concept structure, this database may be particularly useful for testing constructive induction and structure discovery methods. 

Attribute Information:

Class Values:
unacc, acc, good, vgood

Attributes:
buying: vhigh, high, med, low.
maint: vhigh, high, med, low.
doors: 2, 3, 4, 5more.
persons: 2, 4, more.
lug_boot: small, med, big.
safety: low, med, high. 

DataSet 3:- Glass Data Set 

Data Set Information:

Vina conducted a comparison test of her rule-based system, BEAGLE, the nearest-neighbor algorithm, and discriminant analysis. BEAGLE is a product available through VRS Consulting, Inc.; 4676 Admiralty Way, Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189. In determining whether the glass was a type of "float" glass or not, the following results were obtained (# incorrect answers):

Type of Sample -- Beagle -- NN -- DA
Windows that were float processed (87) -- 10 -- 12 -- 21
Windows that were not: (76) -- 19 -- 16 -- 22

The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence...if it is correctly identified!

Attribute Information:

1. Id number: 1 to 214
2. RI: refractive index
3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10)
4. Mg: Magnesium
5. Al: Aluminum
6. Si: Silicon
7. K: Potassium
8. Ca: Calcium
9. Ba: Barium
10. Fe: Iron
11. Type of glass: (class attribute)
-- 1 building_windows_float_processed
-- 2 building_windows_non_float_processed
-- 3 vehicle_windows_float_processed
-- 4 vehicle_windows_non_float_processed (none in this database)
-- 5 containers
-- 6 tableware
-- 7 headlamps

Bayes Classifier:

 1.     BayesNet:

weka.classifiers.bayes

Class BayesNet

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.BayesNet

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, Drawable, OptionHandler, RevisionHandler, WeightedInstancesHandler

Direct Known Subclasses:

BIFReader, EditableBayesNet

public class BayesNet

extends AbstractClassifier

implements OptionHandler, WeightedInstancesHandler, Drawable, AdditionalMeasureProducer

Bayes Network learning using various search algorithms and quality measures.
Base class for a Bayes Network classifier. Provides datastructures (network structure, conditional probability distributions, etc.) and facilities common to Bayes Network learning algorithms like K2 and B.

2.     NaiveBayes:

Class NaiveBayes

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.NaiveBayes

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, Aggregateable<NaiveBayes>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:

NaiveBayesUpdateable

public class NaiveBayes

extends AbstractClassifier

implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler, Aggregateable<NaiveBayes>

Class for a Naive Bayes classifier using estimator classes. Numeric estimator precision values are chosen based on analysis of the training data. For this reason, the classifier is not an UpdateableClassifier (which in typical usage are initialized with zero training instances) -- if you need the UpdateableClassifier functionality, use the NaiveBayesUpdateable classifier. The NaiveBayesUpdateable classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.

3.     NaiveBayesMultinomial:

Class NaiveBayesMultinomial

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.NaiveBayesMultinomial

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

Direct Known Subclasses:

NaiveBayesMultinomialUpdateable

public class NaiveBayesMultinomial

extends AbstractClassifier

implements WeightedInstancesHandler, TechnicalInformationHandler

Class for building and using a multinomial Naive Bayes classifier. 

The core equation for this classifier:
P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)
where Ci is class i and D is a document.

4.     NaiveBayesMultinomialText:  

Class NaiveBayesMultinomialText

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.NaiveBayesMultinomialText

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableBatchProcessor, UpdateableClassifier, Aggregateable<NaiveBayesMultinomialText>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, WeightedInstancesHandler

public class NaiveBayesMultinomialText

extends AbstractClassifier

implements UpdateableClassifier, UpdateableBatchProcessor, WeightedInstancesHandler, Aggregateable<NaiveBayesMultinomialText>

Multinomial naive bayes for text data. Operates directly (and only) on String attributes. Other types of input attributes are accepted but ignored during training and classification

Valid options are:

 -W

  Use word frequencies instead of binary bag of words.

 -P <# instances>

  How often to prune the dictionary of low frequency words (default = 0, i.e. don't prune)

 -M <double>

  Minimum word frequency. Words with less than this frequence are ignored.

  If periodic pruning is turned on then this is also used to determine which

  words to remove from the dictionary (default = 3).

 -normalize

  Normalize document length (use in conjunction with -norm and -lnorm)

 -norm <num>

  Specify the norm that each instance must have (default 1.0)

 -lnorm <num>

  Specify L-norm to use (default 2.0)

 -lowercase

  Convert all tokens to lowercase before adding to the dictionary.

 -stopwords-handler

  The stopwords handler to use (default Null).

 -tokenizer <spec>

  The tokenizing algorihtm (classname plus parameters) to use.

  (default: weka.core.tokenizers.WordTokenizer)

 -stemmer <spec>

  The stemmering algorihtm (classname plus parameters) to use.

 -output-debug-info

  If set, classifier is run in debug mode and

  may output additional info to the console

 -do-not-check-capabilities

  If set, classifier capabilities are not checked before classifier is built

  (use with caution).

4.      Naïve Bayes Multinomial Updatable:

Class NaiveBayesMultinomialUpdateable

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.NaiveBayesMultinomial

weka.classifiers.bayes.NaiveBayesMultinomialUpdateable

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class NaiveBayesMultinomialUpdateable

extends NaiveBayesMultinomial

implements UpdateableClassifier

Class for building and using a multinomial Naive Bayes classifier.

The core equation for this classifier:
P[Ci|D] = (P[D|Ci] x P[Ci]) / P[D] (Bayes rule)
where Ci is class i and D is a document.
Incremental version of the algorithm.

5.      Naïve Bayes Updatable:

Class NaiveBayesUpdateable

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.bayes.NaiveBayes

weka.classifiers.bayes.NaiveBayesUpdateable

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableClassifier, Aggregateable<NaiveBayes>, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class NaiveBayesUpdateable

extends NaiveBayes

implements UpdateableClassifier

Class for a Naive Bayes classifier using estimator classes. This is the updateable version of NaiveBayes.
This classifier will use a default precision of 0.1 for numeric attributes when buildClassifier is called with zero training instances.

Bayes Classifier:

1.      BayesNet:

Classifier	Precision	Recall	F-Measure	Class
DataSet 1	0.795 0.639	0.816    0.608	0.806 0.623	tested_negative tested_positive
DataSet 2	0.918 0.676 0.645 0.938	0.959 0.706 0.290 0.462	0.938 0.690 0.400 0.619	unacc acc good vgood
DataSet 3	0.653 0.767 0.250 0.000 0.750 0.538 0.897	0.886 0.605 0.059 0.000 0.692 0.778 0.897	0.752 0.676 0.095 0.000 0.720 0.636 0.897	build wind float build wind non-float vehic wind float vehic wind non-float containers tableware headlamps
Weighted Avg. DataSet-1	0.741	0.743	0.742
DataSet-2	0.854	0.857	0.849
DataSet-3	0.695	0.706	0.686

Confusion Matrix:

DataSet 1	DataSet 2	DataSet 3
a   b            <-- classified as 408  92 |   a=tested_negative 105 163 |  b=tested_positive	a    b    c    d   <-- classified as 1160   49    1    0 |   a = unacc 104  271    9    0  |   b = acc     0   47   20    2   |   c = good     0   34    1   30   |   d =vgood	a  b c d  e  f  g <--classified as 62  5 2 0 0 1 0 |  a=build wind float  21 46  1  0  3  4  1 |  b = build wind non-float  11  4  1  0  0  1  0 |  c = vehic wind float   0  0  0  0  0  0  0 |  d = vehic wind non-float   0  3  0  0  9  0  1 |  e = containers   0  1  0  0  0  7  1 |  f = tableware   1  1  0  1  0  0 26 |  g = headlamps

2.      NaiveBayes:

Classifier: Naive	Precision	Recall	F-Measure	Class
DataSet 1	0.802 0.678	0.844 0.612	0.823 0.643	tested_negative tested_positive
DataSet 2	0.917 0.672 0.633 0.931	0.960 0.706 0.275 0.415	0.938 0.689 0.384 0.574	unacc acc good vgood
DataSet 3	0.455 0.481 0.190 0.000 0.333 0.571 0.857	0.729 0.171 0.235 0.000 0.308 0.889 0.828	0.560 0.252 0.211 0.000 0.320 0.696 0.842	build wind float build wind non-float vehic wind float vehic wind non-float containers tableware headlamps
Weighted Avg. DataSet-1	0.759	0.763	0.760
DataSet-2	0.852	0.855	0.847
DataSet-3	0.496	0.486	0.453

Confusion Matrix:

DataSet 1	DataSet 2	DataSet 3
a   b         <-- classified as 422  78  |  a=tested_negative 104 164 |  b=tested_positive	a    b    c    d   <-- classified as 1161   48    1    0 |   a = unacc 104  271    9    0  |   b = acc     1   47   19    2   |    c = good     0   37    1   27   |  d = vgood	a  b  c  d  e  f  g   <-- classified as  51  5 11  0  0  2  1 |  a = build wind float  48 13  6  0  5  3  1 |  b = build wind non-float  12  0  4  0  0  1  0 |  c = vehic wind float   0  0  0  0  0  0  0 |  d = vehic wind non-float   0  8  0  0  4  0  1 |  e = containers   0  0  0  0  0  8  1 |  f = tableware   1  1  0  0  3  0 24 |  g = headlamps

3.      NaiveBayesMultinomial:

Classifier: Naive	Precision	Recall	F-Measure	Class
DataSet 1	0.698 0.429	0.678 0.451	0.688 0.440	tested_negative tested_positive
DataSet 2	-------	-------	--------	-------
DataSet 3	0.548 0.460 0.000 0.000 0.500 0.000 0.657	0.657 0.526 0.000 0.000 0.231 0.000 0.793	0.597 0.491 0.000 0.000 0.316 0.000 0.719	build wind float build wind non-float vehic wind float vehic wind non-float containers tableware headlamps
Weighted Avg. DataSet-1	0.604	0.599	0.601
DataSet-2	-----	-----	-------
DataSet-3	0.462	0.523	0.486

Confusion Matrix:

DataSet 1	DataSet 2	DataSet 3
a   b         <-- classified as 339 161 | a=tested_negative 147 121 | b=tested_positive	----------------------------------	a  b  c  d  e  f  g   <-- classified as  46 24  0  0  0  0  0 |  a = build wind float  28 40  0  0  2  2  4 |  b = build wind non-float   9  8  0  0  0  0  0 |  c = vehic wind float   0  0  0  0  0  0  0 |  d = vehic wind non-float   0  5  0  0  3  0  5 |  e = containers   0  6  0  0  0  0  3 |  f = tableware   1  4  0  0  1  0 23 |  g = headlamps

4.      NaiveBayesMultinomialUpdateable:

Classifier: Naive	Precision	Recall	F-Measure	Class
DataSet 1	0.694 0.427	0.686 0.437	0.690 0.432	tested_negative tested_positive
DataSet 2	--------	--------	--------	-------------
DataSet 3	1.000 0.387 0.000 0.000 1.000 0.000 0.938	0.029 0.987 0.000 0.000 0.154 0.000 0.517	0.056 0.556 0.000 0.000 0.267 0.000 0.667	build wind float build wind non-float vehic wind float vehic wind non-float containers tableware headlamps
Weighted Avg. DataSet-1	0.601	0.599	0.600
DataSet-2	------------	------------	---------
DataSet-3	0.652	0.439	0.322

Confusion Matrix:

DataSet 1	DataSet 2	DataSet 3
a   b         <-- classified as 343 157 | a=tested_negative 151 117 | b=tested_positive		a  b  c  d  e  f  g   <-- classified as   2 68  0  0  0  0  0 |  a = build wind float   0 75  0  0  0  0  1 |  b = build wind non-float   0 17  0  0  0  0  0 |  c = vehic wind float   0  0  0  0  0  0  0 |  d = vehic wind non-float   0 11  0  0  2  0  0 |  e = containers   0  9  0  0  0  0  0 |  f = tableware   0 14  0  0  0  0 15 |  g = headlamps

5.      NaiveBayesUpdateable :

Classifier: Naive	Precision	Recall	F-Measure	Class
DataSet 1	0.802 0.678	0.844 0.612	0.823 0.643	tested_negative tested_positive
DataSet 2	0.917 0.672 0.633 0.931	0.960 0.706 0.275 0.415	0.938 0.689 0.384 0.574	unacc acc good vgood
DataSet 3	0.455 0.481 0.190 0.00 0.333 0.571 0.857	0.729 0.171 0.235 0.000 0.308 0.889 0.828	0.560 0.252 0.211 0.000 0.320 0.696 0.842	build wind float build wind non-float vehic wind float vehic wind non-float containers tableware headlamps
Weighted Avg. DataSet-1	0.759	0.763	0.760
DataSet-2	0.852	0.855	0.847
DataSet-3	0.496	0.486	0.453

Confusion Matrix:

DataSet 1	DataSet 2	DataSet 3
a   b         <-- classified as 422  78  | a=tested_negative 104 164 | b=tested_positive	a    b    c    d   <-- classified as 1161   48    1    0 |   a = unacc 104  271    9    0  |   b = acc     1   47   19    2   |   c = good     0   37    1   27   |   d= vgood	a  b  c  d  e  f  g   <-- classified as  51  5 11  0  0  2  1 |  a = build wind float  48 13  6  0  5  3  1 |  b = build wind non-float  12  0  4  0  0  1  0 |  c = vehic wind float   0  0  0  0  0  0  0 |  d = vehic wind non-float   0  8  0  0  4  0  1 |  e = containers   0  0  0  0  0  8  1 |  f = tableware   1  1  0  0  3  0 24 |  g = headlamps

6.      NaiveBayesMultinomialText :

Classifier: Naive	Precision	Recall	F-Measure	Class
DataSet 1	0.651 0.000	1.000 0.000	0.789 0.000	tested_negative tested_positive
DataSet 2	0.700 0.000 0.000 0.000	1.000 0.000 0.000 0.000	0.824 0.000 0.000 0.000	unacc acc good vgood
DataSet 3	0.000 0.355 0.000 0.000 0.000 0.000 0.000	0.000 1.000 0.000 0.000 0.000 0.000 0.000	0.000 0.524 0.000 0.000 0.000 0.000 0.000	build wind float build wind non-float vehic wind float vehic wind non-float containers tableware headlamps
Weighted Avg. DataSet-1	0.424	0.651	0.513
DataSet-2	0.490	0.700	0.577
DataSet-3	0.126	0.355	0.186

Confusion Matrix:

DataSet 1	DataSet 2	DataSet 3
a   b      <-- classified as 500   0 |  a = tested_negative 268   0 |  b = tested_positive	a    b    c    d   <-- classified as 1210    0    0    0 |    a = unacc   384     0    0    0 |    b = acc      69    0    0    0 |    c = good      65    0    0    0 |    d = vgood	a  b  c  d  e  f  g   <-- classified as   0 70  0  0  0  0  0 |  a = build wind float   0 76  0  0  0  0  0 |  b = build wind non-float   0 17  0  0  0  0  0 |  c = vehic wind float   0  0  0  0  0  0  0 |  d = vehic wind non-float   0 13  0  0  0  0  0 |  e = containers   0  9  0  0  0  0  0 |  f = tableware   0 29  0  0  0  0  0 |  g = headlamps